Analysis of the metabolic proteome of lung adenocarcinomas by reverse-phase protein arrays (RPPA) emphasizes mitochondria as targets for therapy

Lung cancer is the leading cause of cancer-related death worldwide despite the success of therapies targeting oncogenic drivers and immune-checkpoint inhibitors. Although metabolic enzymes offer additional targets for therapy, the precise metabolic proteome of lung adenocarcinomas is unknown, hampering its clinical translation. Herein, we used Reverse Phase Protein Arrays to quantify the changes in enzymes of glycolysis, oxidation of pyruvate, fatty acid metabolism, oxidative phosphorylation, antioxidant response and protein oxidative damage in 128 tumors and paired non-tumor adjacent tissue of lung adenocarcinomas to profile the proteome of metabolism. Steady-state levels of mitochondrial proteins of fatty acid oxidation, oxidative phosphorylation and of the antioxidant response are independent predictors of survival and/or of disease recurrence in lung adenocarcinoma patients. Next, we addressed the mechanisms by which the overexpression of ATPase Inhibitory Factor 1, the physiological inhibitor of oxidative phosphorylation, which is an independent predictor of disease recurrence, prevents metastatic disease. We highlight that IF1 overexpression promotes a more vulnerable and less invasive phenotype in lung adenocarcinoma cells. Finally, and as proof of concept, the therapeutic potential of targeting fatty acid assimilation or oxidation in combination with an inhibitor of oxidative phosphorylation was studied in mice bearing lung adenocarcinomas. The results revealed that this therapeutic approach significantly extended the lifespan and provided better welfare to mice than cisplatin treatments, supporting mitochondrial activities as targets of therapy in lung adenocarcinoma patients.

Fisher Scientific, Madrid, Spain). Seventy-two hours after transfection, GFP+ cells were sorted (FACSAriaTM Fusion, BD Biosciences) for selection, and were expanded. All cell lines were tested as negative for mycoplasma contamination.
Cellular lysis and Western blotting. Cell lysis was performed with RLN-T buffer (RLN buffer plus 0.5% Triton X-100 and the complete protease and phosphatase inhibitor cocktails EDTA-free; Roche) at 20×10 6 cells/ml for 15 minutes on ice, freeze-thaw three times in liquid N2 and clarified by centrifugation at 11,000 x g for 15 min at 4°C. Protein concentrations were determined with Bradford reagent using BSA as standard. The resulting supernatants were fractionated on SDS-6%, -9% or -12% PAGE and transferred onto PVDF or nitrocellulose membranes for immunoblot analysis using the primary antibodies and dilutions indicated in Supplemental Table S3 shown in Supplemental Figure S1. Peroxidase conjugated anti-mouse or anti-rabbit IgGs (Nordic Immunology, Rangeerweg, the Netherlands) were diluted in 5% non-fat-dried milk in TBS with 1% Tween 20 and used as secondary antibodies. The Novex® ECL (Cat. No. WP20005, Invitrogen. Madrid,Spain) system was used to visualize the bands. The intensity of the bands was quantified using a GS-900 TM Calibrated Densitometer (Bio-Rad) and ImageJ Software.
RPPA data analysis. WEKA version 3.8 was used for a first inspection and visualization of data (2). Data processing was performed using Python language and Scikit-learn module version 0.20.3 (3). Raw data have been used in order to apply machine-learning methods. As the different markers distribution of each class are unimodal, the distance of each observation to their mode using Mahalanobis distance can be compute in order to identify possible outliers (4). Once the cubic root of the Mahalanobis distance of each observation to the covariance estimation is computed, interquartile range rule (IQR) was used to identify possible outliers and afterwards validated by hand. To exclude the possible existence of some bias, outliers and missing values (less than the 1% of the data) were substituted by a random value generated using a robust covariance estimation and the trusted data of the sample (4). A t-test was applied to evaluate the equality of the means (p < 0.05). Normality of the biomarker's distribution being checked using normality test implemented in Scipy Python modules (5). Hierarchical clustering was performed using the Euclidean distance and Ward linkage methods implemented in Scipy Python modules (6). Pearson correlation matrix analysis and Principal Component Analysis (PCA) was applied to detect biomarker correlations and visualize class variability. Linear Discriminant Analysis (LDA) data transformation was carried out to obtain low-dimensional data that separates the two groups as much as possible, allowing also a graphical representation of the samples. Mostly all clinical data has been classified binarizing continuous values in different ranges. For each data classification, the most useful selection of biomarker combination was performed through two different processes. First, an algorithm has been developed so as to reject biomarkers non-suitable for the classification, then an exhaustive search was performed maximizing LDA classification score. LDA classifiers combined using One-Vs-One strategy was used as classification model. Leave-One-Out Cross-Validation procedure was applied, where statistical measures as sensitivity and specificity were computed besides Receiver Operating Characteristic (ROC) curves. Kaplan-Meir curves/analysis were performed with Lifelines v0.22.9 python module and compared by log-rank test. The Cox proportional hazard regression model was used in determining the value of independent prognostic factors. Cellular O2 consumption and glycolysis rates. Metabolic readouts of different cell lines were performed using substrates at the same concentration in order to maintain comparability. Oxygen consumption rates were determined in A549-Luc, H1975 and PC9 cell lines in a XF24 Extracellular Flux Analyzer (Agilent Technologies, California, USA) using 10 mM glucose, 1 mM pyruvate and 2 mM glutamine (7). For respiration using palmitate as substrate, cells were starved for 12 h in low glucose DMEM (0.05 mM glucose, 1% FBS), and then changed to KHB media (111 mM NaCl, 4.7 mM KCl, 1.25 mM glutamine, 5 mM HEPES, pH 7.4). BSA-conjugated palmitate (1 mM sodium palmitate, 0.17 mM BSA solution) was added as the main substrate. To assess oligomycin sensitive respiration, maximum respiration, and non-mitochondrial dependent oxygen consumption, respectively, 6 μM oligomycin (OL), 0.75 mM 2,4-dinitrophenol (DNP), and 1 μM rotenone plus 1 μM antimycin were added. The initial rates of lactate production were determined as previously described (8).
Mitochondrial membrane potential (ΔΨm) and ROS production. For ΔΨm assessment, transfected A549 and PC9 cells were treated with 100 nM JC1 (Molecular Probes. Madrid,Spain) and 100 nM TMRM (Invitrogen. Madrid,Spain) respectively and processed for flow cytometry (9). ROS production was determined using H2DCFDA (dichlorofluorescin diacetate) (Thermo Fisher, Massachusetts, USA) by flow cytometry (9). The fluorescence intensity of at least 10,000 events was determined in a FACScan cytometer (BD Biosciences) and analyzed using the FLOWJO software (Tree Star, Oregon, USA). Other statistical analysis. The results shown are the means ± SEM. Statistical analysis were performed by Student's t-test and/or one-way ANOVA with the Tukey's or Dunnett's multiple comparisons test. Statistical tests were two-sided at the 5% level of significance. Graphics and statistical analyses were performed using GraphPad Prism 7.

Supplemental Table S5. Comparison of patients' prognosis in LUAD using metabolic proteins as biomarkers in proteomic and transcriptomic studies.
Prognostic significance of the comparison of absolute RPPA values, or Z-score normalized expression of the biomarkers, with transcriptomic studies of the selected genes using the TGCA-Pancancer Atlas and the GDC TCGA databases. The transcriptomic results show no relevant differences in prognosis for seven of the biomarkers studied, the same prediction as in our study for GAPDH, β-F1-ATPase and SOD2, and just the opposite prediction for patients' prognosis for the overexpression of HSP60. The table also incorporates studies that support that the expression of these biomarkers correlate with prognosis of the patients.  Figure S1.